Search

Purpose – This paper aims to address a gap in investigating specific impacts of climate change on mental health in the Pacific region, a region prone to extreme events. This paper reports on a study on the connections between climate change, public health, extreme weather and climate events (EWEs), livelihoods and mental health, focusing on the Pacific region Islands countries. Design/methodology/approach – This paper deploys two main methods. The first is a bibliometric analysis to understand the state of the literature. For example, the input data for term co-occurrence analysis using VOSviewer is bibliometric data of publications downloaded from Scopus. The second method describes case studies, which outline some of the EWEs the region has faced, which have also impacted mental health. Findings – The results suggest that the increased frequency of EWEs in the region contributes to a greater incidence of mental health problems. These, in turn, are associated with a relatively low level of resilience and greater vulnerability. The findings illustrate the need for improvements in the public health systems of Pacific nations so that they are in a better position to cope with the pressures posed by a changing environment. Originality/value – This paper contributes to the current literature by identifying the links between climate change, extreme events, environmental health and mental health consequences in the Pacific Region. It calls for greater awareness of the subject matter of mental health among public health professionals so that they may be better able to recognise the symptoms and relate them to their climate-related causes and co-determinant factors.

Introduction: Artificial Intelligence (AI) is increasingly being integrated into healthcare, particularly through predictive analytics that can enhance patient care and operational efficiency. Nursing leaders play a crucial role in the successful adoption of these technologies. Aim: This study aims to assess the readiness of nursing leaders for AI integration and evaluate their perceptions of the benefits of AI-driven predictive analytics in healthcare. Methods: A descriptive cross-sectional study was conducted among 187 nurse leaders across nine private hospitals in Cairo. The sample was selected using a combination of simple random sampling and non-probability convenience sampling methods to ensure a diverse representation of nursing leadership. Data collection took place from March to May 2024, utilizing a structured questionnaire specifically designed to assess nurse leaders' readiness for AI integration and their perceptions of AI-driven predictive analytics The data were analyzed using IBM SPSS Statistics, version 26.0. Exploratory Factor Analysis (EFA) was employed to identify underlying factors related to readiness and perceived benefits. Confirmatory Factor Analysis (CFA) was subsequently performed to validate the factor structure. Multiple linear regression analysis was conducted to identify significant predictors of AI readiness and perceived benefits. Results: The study revealed that over one-third of nurse leaders exhibited high readiness for AI integration. Significant predictors of readiness included age, educational attainment, and employment status. Positive correlations were found between readiness and perceived benefits of AI, particularly in areas such as care planning and decision-making. Conclusion: The findings suggest that nursing leaders are generally prepared to integrate AI into their workflows, especially those with advanced education and experience. However, further training and policy development are necessary to fully realize the benefits of AI in nursing practice. [ABSTRACT FROM AUTHOR]

Background: In Egypt, approximately 10% of preterm deliveries occur between 32 and fewer than 37 weeks, leading to high neonatal intensive care unit (NICU) admissions. Preterm infants often face oral feeding difficulties due to immature development, which can lead to extended hospital stays and increased health risks. Aim: To assess neonatal nurses' performance in terms of the transition to oral feeding in preterm infants, focusing on knowledge, practices, and attitudes. Methods: A descriptive, quantitative, multicenter, cross-sectional study was conducted across 16 hospitals in five governorates in Egypt from November 2023 to March 2024 involving 553 neonatal nurses. The data were collected through a self-administered questionnaire assessing knowledge and attitudes and through an observed checklist for nurses' practices. The study used statistical methods, including binary logistic regression, to analyze the data. Results: The findings revealed significant knowledge gaps among nurses, particularly in terms of oro-motor function, suck-swallow-breathe patterns, and nonnutritive sucking. A total of 64.6% of the nurses had unsatisfactory knowledge, 58.6% had unsatisfactory practices, and 45% had a negative attitude toward the oral feeding transition. Key predictors of satisfactory practices included higher education levels, full-time employment, and positive attitudes. Conclusion: This study highlights critical gaps in neonatal nurses' knowledge and practices regarding the transition to oral feeding in preterm infants. Addressing these gaps through targeted educational interventions and ongoing support is essential for improving care quality and outcomes for infants. The findings revealed that a substantial proportion of nurses lacked adequate knowledge of critical areas, such as oro-motor function, the suck-swallow-breathe pattern, and nonnutritive sucking. These knowledge deficits could hinder the ability of nurses to provide optimal care during this crucial transition. [ABSTRACT FROM AUTHOR]

The measurement of patients' dosages of radiation caused by medical diagnostics continues to be challenging. A Cantor sequence photonic crystal structure using porous silicon doped with a polymer of polyvinyl alcohol, carbol fuchsin and crystal violet (DPV) is proposed. The influence rules of geometrical and optical parameters such as the radiation doses, number of periods, porosity of porous layers, incident angle and thickness of layers are investigated using MATLAB based on the transfer matrix method. The transmittance of the Cantor sequence of a defective photonic crystal sensor under different conditions is investigated to select the optimum conditions. The proposed system recorded the accepted sensitivity of 0.265 nm/Gy, FoM of 6.5 Gy − 1 , Q of 12,701, RS of 6 × 1 0 − 3 and LoD of 8 × 1 0 − 3 for gamma radiation. The suggested detector has simple design, accurate monitoring efficiency and immense potential for gamma radiation sensing. [ABSTRACT FROM AUTHOR]

It is a very challenging task to solve a nonlinear integral equation in multidimensions. The main purpose of this paper is to develop and analyze a spectral collocation method for a class of nonlinear Fredholm integral equations of the second kind in multidimensions. The proposed spectral collocation method is based on a multivariate Legendre approximation in the frequency space. It is shown by both theory and numerics that the proposed spectral collocation scheme not only significantly reduces the number of degrees of freedom but also produces very accurate approximations. [ABSTRACT FROM AUTHOR]

The present work departs from an extended form of the classical multi-dimensional Gross–Pitaevskii equation, which considers fractional derivatives of the Riesz type in space, a generalized potential function and angular momentum rotation. It is well known that the classical system possesses functionals which are preserved throughout time. It is easy to check that the generalized fractional model considered in this work also possesses conserved quantities, whence the development of conservative and efficient numerical schemes is pragmatically justified. Motivated by these facts, we propose a finite-difference method based on weighted-shifted Grünwald differences to approximate the solutions of the generalized Gross–Pitaevskii system. We provide here a discrete extension of the uniform Sobolev inequality to multiple dimensions, and show that the proposed method is capable of preserving discrete forms of the mass and the energy of the model. Moreover, we establish thoroughly the stability and the convergence of the technique, and provide some illustrative simulations to show that the method is capable of preserving the total mass and the total energy of the generalized system.